Data integration and transformation Paolo Atzeni Dipartimento di Informatica e Automazione Università Roma Tre 29/09/2010

P. AtzeniGII School 29/09/20102 A ten-year goal for database research The “Asilomar report” (Bernstein et al. Sigmod Record –The information utility: make it easy for everyone to store, organize, access, and analyze the majority of human information online A lot of interesting work has been done but … …integration, translation, exchange are still difficult… … 2009 has come… we are late!

P. AtzeniGII School 29/09/20103 A general framework: cooperation "The capacity of a system to interact (effectively) with other systems, possibly managed by different organizations"

P. AtzeniGII School 29/09/20104 Forms of cooperation Process-centered cooperation: –the systems offer services one another, by exchanging messages, information or documents, or by triggering activities, without making remote data explicitly visible Data-centered cooperation: –the systems offer data one another; data is distributed, heterogeneous and autonomous, and accessible from remote locations according to some co-operation agreement

P. AtzeniGII School 29/09/20105 Databases in the Internet era Databases before the Internet –An internal infrastructure, a precious resource, but usually hidden, with some controlled cooperation Internet changes the requirements –More users (not only humans), more diverse cooperating systems (distributed, heterogeneous, autonomous), more types of data "Future" Internet changes more –New devices (embedded everywhere), even more users (many “per person”), real mobility, need for personalization and adaptation –Social networks –Cloud computing applications

P. AtzeniGII School 29/09/20106 The most studied form of data-centered cooperation: integration We are interested in data-centered cooperation, often referred to as integration “The unification of related, heterogeneous data from disparate sources, for example, to enable collaboration” (Hammer & Stonebraker 2005) Some "paradigms" …

P. AtzeniGII School 29/09/20107 Multidatabase Global Manager Local mgr DB Mediator Local mgr DB Mediator Local mgr DB Mediator

P. AtzeniGII School 29/09/20108 Data Warehousing System Mediator Local manager Mediator Local manager Mediator Local manager “Integrator” DB Data Warehouse DW manager

P. AtzeniGII School 29/09/20109 Intermediate solutions in practice Mediator Local manager DB Local manager DB Integrator Mediator Local manager Mediator Local manager DB Local Manager Application

P. AtzeniGII School 29/09/ Peer-based architecture Peer mgr Local mgr DB Local mgr DB Mediator Peer mgr Local mgr DB Local mgr DB Mediator Peer mgr Local mgr DB Local mgr DB Mediator

P. AtzeniGII School 29/09/ Data is not just in databases Mail messages Social networks Web pages Spreadsheets Textual documents Palmtop devices, mobile phones Multimedia annotations (e.g., in digital photos) XML documents

Data spaces The information and data is often unstructured and not preprocessed P. AtzeniGII School 29/09/201012

P. AtzeniGII School 29/09/ The same data in the same form? Adaptivity: –Personalization: content adapted to the user upon system's decision upon user's request –Customization: structure adapted to the user according to the user's role upon user's request –Context dependence User, Device, Network, Place, Time, Rate

P. AtzeniGII School 29/09/ A general need We have data at various places, and data has to be –exchanged –replicated –migrated –integrated –adapted

P. AtzeniGII School 29/09/ A major difficulty Heterogeneity –System level –Model level –Structural (different structure for similar data) –Semantic (different meaning for the same structure) Many efforts, but current techniques are mostly manual and ad hoc

P. AtzeniGII School 29/09/ Three problems Schema and data translation Schema and data integration Data exchange

P. AtzeniGII School 29/09/ Schema and data translation Given a schema find another one with respect to some specific goal (better quality, another model, …)

GII School 29/09/ Many different models OR Relational OO … ER XSD P. Atzeni

GII School 29/09/ Many different models (and variants …) OR w/ PK, gen, ref, FK Relational … OR w/ PK, gen, ref OR w/ PK, gen, FK OR w/ PK, ref, FK OR w/ gen, ref OR w/ PK, FK OR w/ PK, ref OR w/ ref P. Atzeni

GII School 29/09/ Schema and data integration Given two or more sources, build an integrated schema or database

P. AtzeniGII School 29/09/ Data exchange Given a source and a target schema, find a transformation from the former to the latter

P. AtzeniGII School 29/09/ Data exchange, a typical approach (the Clio project) Schema Match Mapping generation Query generation Target schema Source schema

P. AtzeniGII School 29/09/ Data exchange, example PayRate ( Rank HrRate ) Professor ( Id Name Sal ) Student ( Name GPA Yr ) WorksOn ( Name Proj Hrs ProjRank ) Personnel ( Id Name Sal Addr ) Address ( Id Addr )

P. AtzeniGII School 29/09/ Data exchange, example PayRate ( Rank HrRate ) Professor ( Id Name Sal ) Student ( Name GPA Yr ) WorksOn ( Name Proj Hrs ProjRank ) Personnel ( Id Name Sal Addr ) Address ( Id Addr )

P. AtzeniGII School 29/09/ The process, example PayRate ( Rank HrRate ) Professor ( Id Name Sal ) Student ( Name GPA Yr ) WorksOn ( Name Proj Hrs ProjRank ) Personnel ( Id Name Sal Addr ) Address ( Id Addr ) SELECT P.Id, P.Name, P.Sal, A.Addr FROM Professor P, Address A WHERE A.Id = P.Id UNION ALL SELECT NULL AS Id, S.Name, p.HrRate * W.Hrs, NULL AS Addr FROM PayRate P, Student S, WorksOn W WHERE W.Name = S.Name AND S.Yr = P.Rank

P. AtzeniGII School 29/09/ A direction for the solutions Be general! –ad hoc solution could work in-the-small, but they are repetitive and time consuming do not scale are not maintainable Historical notes: –W. C. McGee: Generalization: Key to Successful Electronic Data Processing. J. ACM 1959 Indeed, databases are the result of generalization applied to secondary storage management!

P. AtzeniGII School 29/09/ Generality requires … … high-level descriptions of problems within the family of interest: –Metadata: “data about data” (formal or informal) description of structures and meaning General solutions leverage on metadata (and then operate on data as a consequence)

P. AtzeniGII School 29/09/ A wider perspective (Generic) Model Management: –A proposal by Bernstein et al (2000 +) –Includes a set of operators on schemas and mappings between schemas

P. AtzeniGII School 29/09/ Terminology: a warning Model Mgmt peopleTraditional DB people Meta-metamodelMetamodel Model Schema

P. AtzeniGII School 29/09/ Schemas and mappings More on the issue later For the time being: –Schema: a set of elements, related in some way to one another –Mapping: a set of correspondences (pair of elements) or its reification, a third schema related to the other two via two sets of correspondences

P. AtzeniGII School 29/09/ Model mgmt operators, a first set map = Match (S1, S2) S3 = Merge (S1, S2, map) S2 = Diff (S1, map) and more –map3 = Compose (map1, map2) –S2 = Select (S1, pred) –Apply (S, f) –list = Enumerate (S) –S2 = Copy (S1) –…

P. AtzeniGII School 29/09/ Match map = Match (S1, S2) –given two schemas S1, S2 –returns a mapping between them the “classical” initial step in data integration: –find the common elements of two schemas and the correspondences between them

P. AtzeniGII School 29/09/ Merge S3 = Merge (S1, S2, map) –given two schemas and a mapping between them –returns a third schema (and two mappings) the “classical” second step in data integration: –given the correspondences, find a way to obtain one schema out of two

P. AtzeniGII School 29/09/ Diff S2 = Diff (S1, map) –given a schema and a mapping from it (to some other schema, not relevant) –returns a (sub-)schema, with the elements that do not participate in the mapping

P. AtzeniGII School 29/09/ DW2 Example (Bernstein and Rahm, ER 2000) A database (a “source”), a data warehouse and a mapping between the two We want to add a source, with some similarity to the first one and update the DW DB1 DW1 DB2

P. AtzeniGII School 29/09/ DW2 Example, the "solution" DB1 DW1 DB2 m1 m2 m3 DB2’ m2 = Match(DB1,DB2) m3= Compose(m2,m1) DB2’=Diff(DB2,m3) DW2’, m4 user defined m5 = Match(DW1,DW2’) DW2 = Merge(DW,DW2’,m5) DW2’ m4 m5

P. AtzeniGII School 29/09/ Magic does not exist Operators might require human intervention: –Match is the main case Scripts involving operators might require human intervention as well (or at least benefit from it): –a full implementation of each operator might not always available –a mapping might require manual specification –incomparable alternatives might exist

P. AtzeniGII School 29/09/ The “data level” The major operators have also an extended version that operates on data, and not only on schemas Especially apparent for –Merge

P. AtzeniGII School 29/09/ We also have heterogeneity Round trip engineering (Bernstein, CIDR 2003) –A specification, an implementation –then a change to the implementation: want to revise the specification We need a translation from the implementation model to the specification one S1 I1I2 S2

P. AtzeniGII School 29/09/ Model management with heterogeneity The previous operators have to be “model generic” (capable of working on different models) We need a “translation” operator – = ModelGen (S1)

P. AtzeniGII School 29/09/ ModelGen, an additional operator = ModelGen (S1) –given a schema (in a model) –returns a schema (in a different data model) and a mapping between the two A “translation” from a model to another I should call it “SchemaGen” … We should better write – = ModelGen (S1,mod2)

P. AtzeniGII School 29/09/ S2 Round trip engineering S1 I1 m1 I2 m2 = Match (I1,I2) m3 = Compose (m1,m2) I2’= Diff(I2,m3) = Modelgen(I2’) … Match, Merge m2 m3 I2’ S2’ m4

Summary data management in the Internet world data integration schema and data translation, data exchange model management Part II P. AtzeniGII School 29/09/201043